mcacontour: Contour plots and MCA function for classification modeling

In visualpred: Visualization 2D of Binary Classification Models

Contour plots and MCA function for classification modeling

Description

This function presents visual graphics by means of Multiple correspondence Analysis projection. Interval variables are categorized to bins. Dependent classification variable is set as supplementary variable. Machine learning algorithm predictions are presented in a filled contour setting.

Usage

mcacontour(dataf=dataf,listconti,listclass,vardep,proba="",bins=8,
Dime1="Dim.1",Dime2="Dim.2",classvar=1,intergrid=0,selec=0,
title="",title2="",listacol="",depcol="",alpha1=0.8,alpha2=0.8,alpha3=0.7,modelo="glm",
nodos=3,maxit=200,decay=0.01,sampsize=400,mtry=2,nodesize=5,
ntree=400,ntreegbm=500,shrink=0.01,bag.fraction=1,n.minobsinnode=10,C=100,gamma=10)

Arguments

dataf	data frame.
listconti	Interval variables to use, in format c("var1","var2",...).
listclass	Class variables to use, in format c("var1","var2",...).
vardep	Dependent binary classification variable.
proba	vector of probability predictions obtained externally (optional)
bins	Number of bins for categorize interval variables .
Dime1	FAMD Dimensions to consider. Dim.1 and Dim.2 by default.
Dime2	FAMD Dimensions to consider. Dim.1 and Dim.2 by default.
classvar	1 if dependent variable categories are plotted as supplementary
intergrid	scale of grid for contour:0 if automatic
selec	1 if stepwise logistic variable selection is required, 0 if not.
title	plot main title
title2	plot subtitle
listacol	vector of colors for labels
depcol	vector of two colors for points
alpha1	alpha transparency for majoritary class
alpha2	alpha transparency for minoritary class
alpha3	alpha transparency for fit probability plots
modelo	name of model: "glm","gbm","rf,","nnet","svm".
nodos	nnet: nodes
maxit	nnet: iterations
decay	nnet: decay
sampsize	rf: sampsize
mtry	rf: mtry
nodesize	rf: nodesize
ntree	rf: ntree
ntreegbm	gbm: ntree
shrink	gbm: shrink
bag.fraction	gbm: bag.fraction
n.minobsinnode	gbm:n.minobsinnode
C	svm Radial: C
gamma	svm Radial: gamma

Details

This function applies MCA (Multiple Correspondence Analysis) in order to project points and categories of class variables in the same plot. In addition, interval variables listed in listconti are categorized to the number given in bins parameter (by default 8 bins). Further explanation about machine learning classification and contour curves, see the famdcontour function documentation.

Value

A list with the following objects:

graph1

plot of points on MCA two dimensions

graph2

plot of points and variables

graph3

plot of points and contour curves

graph4

plot of points, contour curves and variables

graph5

plot of points colored by fitted probability

graph6

plot of points colored by abs difference

df1

dataset used for graph1

df2

dataset used for graph2

df3

dataset used for graph3

df4

dataset used for graph4

listconti

interval variables used

listclass

class variables used

...

color schemes and other parameters

Examples

data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-mcacontour(dataf=dataf,listconti,listclass,vardep)

visualpred documentation built on April 3, 2025, 11:08 p.m.